We present 0.5-2keV, 2-8keV, 4-8keV, and 0.5-8keV (hereafter soft, hard, ultra-hard, and full bands, respectively) cumulative and differential number-count (log N-log S) measurements for the recently completed 4Ms Chandra Deep Field-South (CDF-S) survey, the deepest X-ray survey to date. We implement a new Bayesian approach, which allows reliable calculation of number counts down to flux limits that are factors of 1.9-4.3times fainter than the previously deepest number-count investigations. In the soft band (SB), the most sensitive bandpass in our analysis, the 4Ms CDF-S reaches a maximum source density of 27,800deg-2. By virtue of the exquisite X-ray and multiwavelength data available in the CDF-S, we are able to measure the number counts from a variety of source populations (active galactic nuclei (AGNs), normal galaxies, and Galactic stars) and subpopulations (as a function of redshift, AGN absorption, luminosity, and galaxy morphology) and test models that describe their evolution. We find that AGNs still dominate the X-ray number counts down to the faintest flux levels for all bands and reach a limiting SB source density of 14,900deg-2, the highest reliable AGN source density measured at any wavelength. We find that the normal-galaxy counts rise rapidly near the flux limits and, at the limiting SB flux, reach source densities of 12,700deg -2 and make up 46% ± 5% of the total number counts. The rapid rise of the galaxy counts toward faint fluxes, as well as significant normal-galaxy contributions to the overall number counts, indicates that normal galaxies will overtake AGNs just below the 4Ms SB flux limit and will provide a numerically significant new X-ray source population in future surveys that reach below the 4Ms sensitivity limit. We show that a future 10Ms CDF-S would allow for a significant increase in X-ray-detected sources, with many of the new sources being cosmologically distant (z ≳ 0.6) normal galaxies.
All Science Journal Classification (ASJC) codes
- Astronomy and Astrophysics
- Space and Planetary Science